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Record W2324639334 · doi:10.1021/ma502021s

Electroacoustic Spectroscopy of Nanoparticle-Doped Hydrogels

2014· article· en· W2324639334 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMacromolecules · 2014
Typearticle
Languageen
FieldChemistry
TopicElectrostatics and Colloid Interactions
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicrorheologySelf-healing hydrogelsRheometryRheologyElectrokinetic phenomenaMaterials scienceDynamic mechanical analysisNanoparticleChemical engineeringSpectroscopyElectrophoresisParticle sizePolymerAnalytical Chemistry (journal)ChemistryNanotechnologyPolymer chemistryComposite materialChromatography

Abstract

fetched live from OpenAlex

This paper probes the nanoparticle (NP) interaction with hydrogels using electroacoustic spectroscopy at MHz frequencies. We measured dynamic electrophoretic mobility spectra of silica NPs in polyacrylamide gels for a variety of NP sizes and gel concentrations. The spectra are exquisitely sensitive to NP entrapment, size, and charge as well as to gel rheology and gelation kinetics. For NPs that are large compared to the gel mesh size, many of these influences can be quantified using electrokinetic theory, which furnishes the apparent NP ζ-potential and a complex gel shear modulus at MHz frequencies. The methodology provides new insights into the NP–hydrogel interaction, since it noninvasively probes the nanostructure and the combined influences of particle and gel properties. Electroacoustic spectroscopy may therefore be a valuable new tool for characterizing soft nanocomposites—one that complements other noninvasive methods, such as bulk rheometry and microrheology.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.876

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.234
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it